Dataset Information

Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection.

ABSTRACT:

Background

The aim of this secondary analysis of the TESEO cohort is to identify, early in the course of treatment with tocilizumab, factors associated with the risk of progressing to mechanical ventilation and death and develop a risk score to estimate the risk of this outcome according to patients' profile.

Methods

Patients with COVID-19 severe pneumonia receiving standard of care + tocilizumab who were alive and free from mechanical ventilation at day 6 after treatment initiation were included in this retrospective, multicenter cohort study. Multivariable logistic regression models were built to identify predictors of mechanical ventilation or death by day-28 from treatment initiation and ?-coefficients were used to develop a risk score. Secondary outcome was mortality. Patients with the same inclusion criteria as the derivation cohort from 3 independent hospitals were used as validation cohort.

Results

266 patients treated with tocilizumab were included. By day 28 of hospital follow-up post treatment initiation, 40 (15%) underwent mechanical ventilation or died [26 (10%)]. At multivariable analysis, sex, day-4 PaO2/FiO2 ratio, platelets and CRP were independently associated with the risk of developing the study outcomes and were used to generate the proposed risk score. The accuracy of the score in AUC was 0.80 and 0.70 in internal validation and test for the composite endpoint and 0.92 and 0.69 for death, respectively.

Conclusions

Our score could assist clinicians in identifying, early after tocilizumab administration, patients who are likely to progress to mechanical ventilation or death, so that they could be selected for eventual rescue therapies.

SUBMITTER: Mussini C

PROVIDER: S-EPMC7901750 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection.

Mussini Cristina C Cozzi-Lepri Alessandro A Menozzi Marianna M Meschiari Marianna M Franceschini Erica E Milic Jovana J Brugioni Lucio L Pietrangelo Antonello A Girardis Massimo M Cossarizza Andrea A Tonelli Roberto R Clini Enrico E Massari Marco M Bartoletti Michele M Ferrari Anna A Cattelan Anna Maria AM Zuccalà Paola P Lichtner Miriam M Rossotti Roberto R Girardi Enrico E Nicastri Emanuele E Puoti Massimo M Antinori Andrea A Viale Pierluigi P Guaraldi Giovanni G

PloS one 20210223 2

<h4>Background</h4>The aim of this secondary analysis of the TESEO cohort is to identify, early in the course of treatment with tocilizumab, factors associated with the risk of progressing to mechanical ventilation and death and develop a risk score to estimate the risk of this outcome according to patients' profile.<h4>Methods</h4>Patients with COVID-19 severe pneumonia receiving standard of care + tocilizumab who were alive and free from mechanical ventilation at day 6 after treatment initiati ...[more]

PMID: 33621264

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Project description:ObjectivesWe aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19).MethodsWe performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: Spo2 <93% with 100% Fio2, respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, β-coefficients were used to develop a risk score. Trial Registration NCT04316949.ResultsWe analysed 1113 patients (644 derivation, 469 validation cohort). Mean (±SD) age was 65.7 (±15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age ≥70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature ≥38°C (OR 1.73; 95% CI 1.30-2.29), respiratory rate ≥22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes ≤900 cells/mm3 (OR 2.69; 95% CI 1.60-4.51), creatinine ≥1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein ≥10 mg/dL (OR 5.91; 95% CI 4.88-7.17) and lactate dehydrogenase ≥350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81-0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively.ConclusionPREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic.

Project description:ObjectivesAccurate estimation of the risk of SARS-CoV-2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13-item CORC (COVID [or coronavirus] Rule-out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter-rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt.MethodsThis noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS-CoV-2 test result and confidence (0%-100%). The criterion standard for SARS-CoV-2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs).ResultsFor 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579-0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591-0.697). The agreement for clinician gestalt binary determination of SARs-CoV-2 status was κ = 0.534 (95% CI = 0.437-0.632) with median confidence of 76% (first-third quartile = 66-88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR-) of 0.24 (95% CI = 0.10-0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS-CoV-2 test was 58% (95% CI = 28%-85%) and positive LR was 13.1 (95% CI = 4.5-37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR- of 0.57 (95% CI = 0.39-0.74).ConclusionIn this prospective study, the CORC score and rule demonstrated good inter-rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs-CoV-2 infection.

Dataset Information

Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection.

Background

Methods

Results

Conclusions

Publications

Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection.

Similar Datasets

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